keras-unet-collection:Tensorflow，Keras U-net，V-net，U-net ++，R2U-...

# keras-unet-collection [](https://badge.fury.io/py/keras-unet-collection) [](https://pypi.org/project/keras-unet-collection/) [](https://github.com/yingkaisha/keras-unet-collection/graphs/commit-activity) The `tensorflow.keras` implementation of U-net, V-net, U-net++, R2U-net, Attention U-net, ResUnet-a, U^2-Net, and UNET 3+ with optional ImageNet-trained backbones. ---------- `keras_unet_collection.models` contains functions that configure keras models with hyper-parameter options. * Pre-trained ImageNet backbones are supported for U-net, U-net++, Attention U-net, and UNET 3+. * Deep supervision is supported for U-net++, UNET 3+, and U^2-Net. * See the [User guide](https://github.com/yingkaisha/keras-unet-collection/blob/main/examples/user_guid_models.ipynb) for other options and use cases. | `keras_unet_collection.models` | Name | Reference | |:---------------|:----------------|:----------------| | `unet_2d` | U-net | [Ronneberger et al. (2015)](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28) | | `vnet_2d` | V-net (modified for 2-d inputs) | [Milletari et al. (2016)](https://arxiv.org/abs/1606.04797) | | `unet_plus_2d` | U-net++ | [Zhou et al. (2018)](https://link.springer.com/chapter/10.1007/978-3-030-00889-5_1) | | `r2_unet_2d` | R2U-Net | [Alom et al. (2018)](https://arxiv.org/abs/1802.06955) | | `att_unet_2d` | Attention U-net | [Oktay et al. (2018)](https://arxiv.org/abs/1804.03999) | | `resunet_a_2d` | ResUnet-a | [Diakogiannis et al. (2020)](https://doi.org/10.1016/j.isprsjprs.2020.01.013) | | `u2net_2d` | U^2-Net | [Qin et al. (2020)](https://arxiv.org/abs/2005.09007) | | `unet_3plus_2d` | UNET 3+ | [Huang et al. (2020)](https://arxiv.org/abs/2004.08790) | ---------- ` keras_unet_collection.base` contains functions that build the base architecture (i.e., without model heads) of Unet variants for model customization and debugging. | ` keras_unet_collection.base` | Notes | |:-----------------------------------|:------| | `unet_2d_base`, `vnet_2d_base`, `unet_plus_2d_base`, `r2_unet_2d_base`, `att_unet_2d_base`, `resunet_a_2d_base`, `u2net_2d_base`, `unet_3plus_2d_base` | Functions that accept an input tensor and hyper-parameters of the corresponded model, and produce output tensors of the base architecture. | ---------- `keras_unet_collection.activations` and `keras_unet_collection.losses` provide additional activation layers and loss functions. | `keras_unet_collection.activations` | Name | Reference | |:--------|:----------------|:----------------| | `GELU` | Gaussian Error Linear Units (GELU) | [Hendrycks et al. (2016)](https://arxiv.org/abs/1606.08415) | | `Snake` | Snake activation | [Liu et al. (2020)](https://arxiv.org/abs/2006.08195) | | `keras_unet_collection.losses` | Name | Reference | |:----------------|:----------------|:----------------| | `dice` | Dice loss | [Sudre et al. (2017)](https://link.springer.com/chapter/10.1007/978-3-319-67558-9_28) | | `tversky` | Tversky loss | [Hashemi et al. (2018)](https://ieeexplore.ieee.org/abstract/document/8573779) | | `focal_tversky` | Focal Tversky loss | [Abraham et al. (2019)](https://ieeexplore.ieee.org/abstract/document/8759329) | | `triplet_1d` | Semi-hard triplet loss (experimental) | | | `crps2d_tf` | CRPS loss (experimental) | | # Installation and usage ```pip install keras-unet-collection``` ```python from keras_unet_collection import models # e.g. models.unet_2d(...) ``` * **Note**: Currently supported backbone models are: `VGG[16,19]`, `ResNet[50,101,152]`, `ResNet[50,101,152]V2`, `DenseNet[121,169,201]`, and `EfficientNetB[0-7]`. See [Keras Applications](https://keras.io/api/applications/) for details. * **Note**: This package is planned for major updates. For versions prior to 0.1.0, backward compatibility is not ensured. * **Note**: Neural networks produced by this package may not be compatible with other pre-trained models of the same name. Training from scratch is recommended. * Jupyter notebooks are provided as [examples](https://github.com/yingkaisha/keras-unet-collection/tree/main/examples): * Attention U-net with VGG16 backbone [[link]](https://github.com/yingkaisha/keras-unet-collection/blob/main/examples/human-seg_atten-unet-backbone_coco.ipynb). * UNET 3+ with deep supervision and classification guided module [[link]](https://github.com/yingkaisha/keras-unet-collection/blob/main/examples/segmentation_unet-three-plus_oxford-iiit.ipynb). * [Changelog](https://github.com/yingkaisha/keras-unet-collection/blob/main/CHANGELOG.md) # Dependencies * TensorFlow 2.3.0, Keras 2.4.0, Numpy 1.18.2. * (Optional for examples) Pillow, matplotlib, etc. # Overview U-net is a convolutional neural network with encoder-decoder architecture and skip-connections, loosely defined under the concept of "fully convolutional networks." U-net was originally proposed for the semantic segmentation of medical images and is modified for solving a wider range of gridded learning problems. U-net and many of its variants take three or four-dimensional tensors as inputs and produce outputs of the same shape. One technical highlight of these models is the skip-connections from downsampling to upsampling layers, which benefit the reconstruction of high-resolution, gridded outputs. # Contact Yingkai (Kyle) Sha <<yingkai@eoas.ubc.ca>> <<yingkaisha@gmail.com>> # License [MIT License](https://github.com/yingkaisha/keras-unet/blob/main/LICENSE)